fake_news_english / README.md
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids: []
pretty_name: Fake News English
dataset_info:
  features:
    - name: article_number
      dtype: int32
    - name: url_of_article
      dtype: string
    - name: fake_or_satire
      dtype:
        class_label:
          names:
            '0': Satire
            '1': Fake
    - name: url_of_rebutting_article
      dtype: string
  splits:
    - name: train
      num_bytes: 78070
      num_examples: 492
  download_size: 43101
  dataset_size: 78070
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for Fake News English

Table of Contents

Dataset Description

Dataset Summary

This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

English

Dataset Structure

Data Instances

{
"article_number": 102 ,
"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
"fake_or_satire": 1,  # Fake
"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
}

Data Fields

  • article_number: An integer used as an index for each row
  • url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
  • fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
  • url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)

Data Splits

This dataset is not split, only the train split is available.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Golbeck, Jennifer Everett, Jennine Falak, Waleed Gieringer, Carl Graney, Jack Hoffman, Kelly Huth, Lindsay Ma, Zhenya Jha, Mayanka Khan, Misbah Kori, Varsha Mauriello, Matthew Lewis, Elo Mirano, George IV, William Mussenden, Sean Nelson, Tammie Mcwillie, Sean Pant, Akshat Cheakalos, Paul

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{inproceedings, author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul}, year = {2018}, month = {05}, pages = {17-21}, title = {Fake News vs Satire: A Dataset and Analysis}, doi = {10.1145/3201064.3201100} }

Contributions

Thanks to @MisbahKhan789, @lhoestq for adding this dataset.